There are two main methods for recording cell activity using large cellular (neural) interfaces comprising at least 128×128 sensors.
A first approach is based on a scanning method of the entire array of sensors.
One example of this method is disclosed in Eversmann et al., “A 128×128 CMOS Biosensor Array for Extracellular Recording of Neural Activity,” IEEE Journal of Solid State Circuits, Vol. 38, No. 12, December 2003. In this document, the authors describe a method and a device in which there are 128 rows and 128 columns. The entire array of sensors is individually scanned at a sufficient scanning frequency (2 kiloframes per second, (kfps)). The scanning is done with a row and column addressing technique. Each row has one amplifier leading to a total of 128 amplifiers. These are further multiplexed 8 to 1 and thus there are 16 outputs. This information is sent off the chip to a PC where 16 A/D converters are used and the information is processed.
The amount of information to be processed is quite large, 32 MS/s (MegaSamples/second). This raises important problems for post processing of the information. As arrays get larger the amount of data to be processed also increases. An array of 256×256 sensors would require 131 MS/s. Furthermore, noise is also a concern as a recording amplifier has to be present in every sensor, the size of which has to be limited to keep the pitch of the sensor within a reasonable value; comparable to the size of a neuron (between about 20 μm and about 100 μm).
The second method is based on the assumption that not all sensors are in contact with a cell (neuron) and are therefore not of interest.
An example of this method is disclosed in U. Frey et al., “An 11k-electrode 126-channel High Density Microelectrode Array To Interact With Electrogenic Cells,” in ISSCC 2007, San Francisco, Calif., February 2007, pp. 158-159, wherein, the authors describe a very large array with 11,000 sensors having 126 channels that are permanently routed to sensors of interest. The amount of data is consequently reduced and noise performance is improved as the sensors only require a routing circuitry.
Because of the scarcity of neurons on the sensors, the system is capable of recording most of the cellular activity. Nevertheless, all of the neurons are not observable, and the device is always limited to the number of channels available for routing. In this case, only 1.14% of the array is observable at any one time. Increasing the number of sensors will require an increase in the number of channels. Assuming a linear increase, for a 256×256 array, 504 channels will be required for the same system monitoring capability.
An overview of the methods used according to the state of the art is represented in FIG. 1.